title: Nonparametric tests for homogeneity of species assemblages: a data depth approach.
abstract: Testing homogeneity of species assemblages has important applications in ecology. Due to the unique structure of abundance data often collected in ecological studies, most classical statistical tests cannot be applied directly. In this article, we propose two novel nonparametric tests for comparing species assemblages based on the concept of data depth. They can be considered as a natural generalization of the Kolmogorov-Smirnov and the Cramér-von Mises tests (KS and CM) in this species assemblage comparison context. Our simulation studies show that the proposed test is more powerful than other existing methods under various settings. A real example is used to demonstrate how the proposed method is applied to compare species assemblages using plant community data from a highly diverse tropical forest at Barro Colorado Island, Panama.
authors.affiliation: Department of Statistics, University of California, Riverside, California 92521, USA. jun.li@ucr.edu
authors.forename: Jun
authors.initials: J
authors.lastname: Li
authors.forename: Jifei
authors.initials: J
authors.lastname: Ban
authors.forename: Louis S
authors.initials: LS
authors.lastname: Santiago
mesh_terms: D044822:Biodiversity; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004463:Ecology / Q000706:statistics & numerical data*; D017753:Ecosystem; D015233:Models, Statistical; D011157:Population Dynamics; D014197:Trees / Q000145:classification*